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The TensorFlow Workshop

You're reading from   The TensorFlow Workshop A hands-on guide to building deep learning models from scratch using real-world datasets

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781800205253
Length 600 pages
Edition 1st Edition
Languages
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Authors (4):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Abhranshu Bagchi Abhranshu Bagchi
Author Profile Icon Abhranshu Bagchi
Abhranshu Bagchi
Anthony Maddalone Anthony Maddalone
Author Profile Icon Anthony Maddalone
Anthony Maddalone
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
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Toc

Table of Contents (13) Chapters Close

Preface
1. Introduction to Machine Learning with TensorFlow 2. Loading and Processing Data FREE CHAPTER 3. TensorFlow Development 4. Regression and Classification Models 5. Classification Models 6. Regularization and Hyperparameter Tuning 7. Convolutional Neural Networks 8. Pre-Trained Networks 9. Recurrent Neural Networks 10. Custom TensorFlow Components 11. Generative Models Appendix

Deep Convolutional Generative Adversarial Networks (DCGANs)

DCGANs use convolutional neural networks instead of simple neural networks for both the discriminator and the generator. They can generate higher-quality images and are commonly used for this purpose.

The generator is a set of convolutional layers with fractional stride convolutions, also known as transpose convolutions. Layers with transpose convolutions upsample the input image at every convolutional layer, which increases the spatial dimensions of the images after each layer.

The discriminator is a set of convolutional layers with stride convolutions, so it downsamples the input image at every convolutional layer, decreasing the spatial dimensions of the images after each layer.

Consider the following two images. Can you identify which one is fake and which one is real? Take a moment and look carefully at each of them.

Figure 11.19: Face example

You may be surprised to find out that neither...

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